Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
Yi Zhang, Lei Sang, Yiwen Zhang

TL;DR
This paper introduces BIGCF, a novel graph collaborative filtering framework that models individual and collective intents behind user interactions, addressing feedback sparsity and capturing nuanced user preferences for improved recommendations.
Contribution
The paper proposes a bilateral intent-guided approach with Gaussian-based graph generation and contrastive regularization, advancing intent modeling in recommender systems.
Findings
BIGCF outperforms existing methods on three real-world datasets.
The approach effectively captures both individual and collective user intents.
Graph contrastive regularization improves the robustness of intent and interaction representations.
Abstract
Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation results. The current mainstream modeling method is to abstract the intent into unknowable but learnable shared or non-shared parameters. Despite considerable progress, we argue that it still confronts the following challenges: firstly, these methods only capture the coarse-grained aspects of intent, ignoring the fact that user-item interactions will be affected by collective and individual factors (e.g., a user may choose a movie because of its high box office or because of his own unique preferences); secondly, modeling believable intent is severely hampered by implicit feedback, which is incredibly sparse and devoid of true semantics. To address these challenges, we propose a novel recommendation framework…
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Taxonomy
TopicsTechnology Adoption and User Behaviour · Knowledge Management and Sharing · Innovative Human-Technology Interaction
